https://github.com/rohanshad/cmr_transformer
Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
Papers with Code 아카이브
NER
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The named entity recognition (NER) involves identification of key information in the text and classification into a set of predefined categories. This includes standard entities in the text like Part of Speech (PoS) and entities like places, names etc...
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/sayef/fsner
https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Sequence-Labeling
Empower Sequence Labeling with Task-Aware Neural Language Model
https://github.com/shreyanse081/Hindi_NER_monikkinom
Towards Deep Learning in Hindi NER: An approach to tackle the Labelled Data Scarcity
https://github.com/solkx/tkdp
TKDP: Threefold Knowledge-enriched Deep Prompt Tuning for Few-shot Named Entity Recognition
https://github.com/sreejukomath/NLPProjects
https://github.com/stefan-it/fine-tuned-berts-seq
Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT
https://github.com/taidnguyen/software_entity_recognition
https://github.com/thespectrewithin/joint_align
Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework
https://github.com/tonygsw/Joint-Extraction-of-Entities-and-Relations-Based-on-a-Novel-Tagging-Scheme
https://github.com/twitter-research/multilingual-alignment-tpp
https://github.com/uds-lsv/Training-a-Neural-Network-in-a-Low-Resource-Setting-on-Automatically-Annotated-Noisy-Data
Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data
https://github.com/ufal/acl2019_nested_ner
https://github.com/uloveqian2021/NER
CLUENER2020: Fine-grained Named Entity Recognition Dataset and Benchmark for Chinese
https://github.com/universal-ner/universal-ner
UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition
https://github.com/vedantc6/mtl-dts
Deeper Task-Specificity Improves Joint Entity and Relation Extraction
https://github.com/vincenzo-scotti/def_2_vec
https://github.com/vinhdv1628/etnlp
ETNLP: a visual-aided systematic approach to select pre-trained embeddings for a downstream task
https://github.com/voidful/NLPrep
https://github.com/yangyucheng000/bert_Ascend
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
https://github.com/ye-kyaw-thu/preprints
https://github.com/yuchenlin/CDMA-NER
Neural Adaptation Layers for Cross-domain Named Entity Recognition
https://github.com/yuwfan/FILTER
FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding
https://github.com/zhangyuwangumass/Glyph-based-Chinese-Character-Embedding
https://github.com/zjuym/chinese_cws_ner
Fast and Accurate Entity Recognition with Iterated Dilated Convolutions
https://github.com/zysite/post
Empower Sequence Labeling with Task-Aware Neural Language Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.